Mask Approximation Net: A Novel Diffusion Model Approach for Remote Sensing Change Captioning

Remote sensing (RS) image change description represents an innovative multimodal task within the realm of RS processing. This task not only facilitates the detection of alterations in surface conditions but also provides comprehensive descriptions of these changes, thereby improving human interpretability and interactivity. Current deep learning methods typically adopt a three-stage framework consisting of feature extraction, feature fusion, and change localization, followed by text generation. Most approaches focus heavily on designing complex network modules but lack solid theoretical guidance, relying instead on extensive empirical experimentation and iterative tuning of network components. This experience-driven design paradigm may lead to overfitting and design bottlenecks, thereby limiting the model’s generalizability and adaptability. To address these limitations, this article proposes a paradigm that shifts toward data distribution learning using diffusion models, reinforced by frequency-domain noise filtering, to provide a theoretically motivated and practically effective solution to multimodal RS change description. The proposed method primarily includes a simple multiscale change detection (CD) module, whose output features are subsequently refined by a well-designed diffusion model. Furthermore, we introduce a frequency-guided complex filter module to boost the model’s performance by managing high-frequency noise throughout the diffusion process. We validate the effectiveness of our proposed method across several datasets for RS CD and description, showcasing its superior performance compared to existing techniques.